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PenTest MCP

PenTest MCP

by anishxagrawal
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

graphql_cop

Scan GraphQL APIs for security vulnerabilities, detecting injection flaws, misconfigurations, and authorization issues to harden endpoints before attackers exploit them.

Instructions

GraphQL security scanner

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>",
      "session_id": "<session_id>"
    },
    "name": "graphql_cop"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYes
dry_runNoPreview commands/steps without executing the scan
session_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It simply says 'GraphQL security scanner', giving no indication of whether the tool is read-only, potentially destructive, or what network requests it makes. For a security scanner that may send payloads, this omission is significant.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise but severely under-specified for a tool with three parameters and no annotations. Important context is missing, and the brevity sacrifices completeness. Conciseness should not come at the cost of utility.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and low parameter coverage, the description is grossly incomplete. It fails to explain what the scan does, how results are returned, or any constraints. An AI agent would lack the information needed to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33% (only 'dry_run' has a description). The tool description adds no parameter information. The 'url' and 'session_id' parameters are completely undocumented in the description, and the agent needs to infer their purpose from names alone. The description does not compensate for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a 'GraphQL security scanner', which indicates the tool scans GraphQL endpoints for security issues. This distinguishes it from sibling tools like sqlmap (SQL injection) and nuclei (general scanning). However, it could be more specific about the type of security scanning (e.g., vulnerabilities, misconfigurations).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description lacks context about prerequisites (e.g., target must be a GraphQL endpoint) or when not to use it. The sibling tools cover a wide range of security scans, and without explicit guidance, an AI agent may select it inappropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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